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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A python interface for Grappler items."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.core.grappler.costs import op_performance_data_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python import pywrap_tensorflow as tf_item
from tensorflow.python.framework import errors
class Item(object):
"""GrapplerItem."""
def __init__(self,
metagraph,
ignore_colocation=True,
ignore_user_placement=False):
"""Creates an Item.
Args:
metagraph: a TensorFlow metagraph.
ignore_colocation: if set, the tool will ignore all the colocation
constraints generated by TensorFlow.
ignore_user_placement: if set, all the placement annotations annotated in
the metagraph will be ignored.
Raises:
ValueError: the metagraph is incomplete or invalid.
"""
self._metagraph = metagraph
self._item_graph = meta_graph_pb2.MetaGraphDef()
self._item_graph.CopyFrom(metagraph)
self._ignore_colocation = ignore_colocation
self._ignore_user_placement = ignore_user_placement
self._tf_item = None
self._BuildTFItem()
def IdentifyImportantOps(self, sort_topologically=False):
return tf_item.TF_IdentifyImportantOps(self.tf_item, sort_topologically)
def GetOpProperties(self):
ret_from_swig = tf_item.TF_GetOpProperties(self.tf_item)
properties = {}
for key, values in ret_from_swig.items():
prop = []
for value in values:
prop.append(
op_performance_data_pb2.OpInfo.TensorProperties.FromString(value))
properties[key] = prop
return properties
def GetColocationGroups(self):
"""Return a list of hard colocation constraints.
All the nodes in a colocation tuple must be placed on the same device for
the model to work.
Returns:
A list of colocation tuples.
"""
return tf_item.TF_GetColocationGroups(self.tf_item)
@property
def metagraph(self):
return self._metagraph
@property
def tf_item(self):
if self._item_graph != self._metagraph:
self._BuildTFItem()
self._item_graph.CopyFrom(self._metagraph)
return self._tf_item
def _BuildTFItem(self):
with errors.raise_exception_on_not_ok_status() as status:
self._tf_item = tf_item.TF_NewItem(self._metagraph.SerializeToString(),
self._ignore_colocation,
self._ignore_user_placement, status)
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